The Role of Emotional Intelligence in Engagement in Nurses

Aware that engagement in the healthcare field needs high levels of emotional intelligence, we began this study to determine relationship between engagement and emotional intelligence in nurses. The objective of this study was to determine the explanatory value of the components of emotional intelligence for engagement in a sample of nurses. The final study sample was made up of 2126 working nursing professionals. Data was obtained by distributing, an ad hoc questionnaire was used to collect sociodemographic information, and to collect professional and employment information, the Utrecht Work Engagement Scale and the Reduced Emotional Intelligence Inventory for Adults were used. The results showed that nurses with higher levels of emotional intelligence also scored more highly in engagement, with the interpersonal factor being the greatest predictor of engagement. This study has significant practical implications for the creation of intervention programs and activities to improve the performance of nurses in the workplace.


Introduction
Professional practice in healthcare requires a lot of personal and organizational engagement. Nurses perform many different care and treatment activities with the primary aim of contributing to the promotion, stabilization, and maintenance of their patients' health. Using a broad concept of health [1], understood as a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity, engagement becomes a fundamental variable for quality patient care [2][3][4].
Engagement has been empirically shown to influence nursing performance, with a consequent impact on health care results [5]. From a psychological point of view, engagement leads to subjective wellbeing [6] as it allows an individual to enter a flow state [7] and satisfy basic psychological needs of autonomy and competence [8,9]. Previous research in nursing has confirmed a positive relationship between engagement and self-efficacy as well as job satisfaction [10,11]. Research has also found significant associations between engagement and personal factors such as mental health, locus of control, and job satisfaction [12,13].
Engagement has been defined by three fundamental dimensions: vigor, dedication, and absorption [14]. Vigor is characterized by high levels of energy and mental resilience in the face of difficulties and implies effort and persistence at work. Dedication is defined as being closely involved in one's work, has a cognitive dimension or belief in what one is doing, and an affective dimension, related to feelings of enthusiasm, inspiration, pride, challenge, and significance. Absorption is characterized by a state of abstraction at work, experiencing a feeling of enjoyment associated with Starting from these premises, and aware that engagement in the healthcare field needs high levels of emotional intelligence, we began this study into the relationship between engagement and emotional intelligence in nurses. We proposed the following objective: to determine the explanatory value of the components of emotional intelligence for engagement in a sample of nurses.
We began with the following hypotheses: (1) despite the literature review not producing conclusive evidence, we expected to find differences in emotional intelligence and engagement according to sociodemographic variables, principally sex and age; (2) we expected to find significant positive correlations between emotional intelligence and engagement in nurses; and (3) the emotional intelligence dimensions of stress management, mood, and interpersonal factor will have the greatest predictive value for engagement in nurses.

Participants
The initial sample was made up of 2218 nurses from Andalucía (Spain) who were randomly selected from various centers. We identified 92 cases that were removed from the sample for not completing the whole questionnaire (32 subjects) or because we found that they had completed it randomly (60 subjects). As the main variable in the study was engagement, the selection of participants included noting their current working situation (permanent or temporary contracts). The resulting sample was made up of 2126 working nursing professionals (69.6% with temporary contracts, n = 1479, and 30.4% with permanent contracts, n = 647).

Instruments
We created an ad hoc questionnaire to collect sociodemographic data (age, sex, number of children, type of work contract).
The Utrecht Work Engagement Scale (UWES) [20]; is a self-reporting scale to evaluate engagement at work through 17 items with a seven-point Likert-type response scale. It produces information about three aspects of engagement: vigor (e.g., "At my work, I feel bursting with energy"), dedication (e.g., "I find the work that I do full of meaning and purpose"), and absorption (e.g., "Time flies when I'm working"). The scale gives an overall engagement score and a score for each of the specific dimensions. This instrument has demonstrated appropriate reliability and validity [14]. In our sample of nurses, the internal reliability indices in each of the dimensions were very good, with a Chronbach's alpha of 0.84 in the vigor dimension, 0.89 in dedication, and 0.81 in absorption.
The Reduced Emotional Intelligence Inventory for Adults (EQ-i-20M) [39] was validated and assessed by the authors for the adult Spanish population, and derived from the adaptation for adults of the Emotional Intelligence Inventory: Young Version (EQ-i-YV) from Bar-On and Parker [40]. It consists of 20 items with four response alternatives in Likert-type scales. It was structured as five factors: intrapersonal (area that includes the following components: emotional understanding, of self, assertiveness, self-concept, self-realization, and independence; e.g., "I can describe my feelings easily"); interpersonal (empathy, social responsibility and interpersonal relationship; e.g., "I understand well how other people feel"); stress management (stress tolerance and impulse control; e.g., "I find it hard to control my anger"); adaptability (proof of reality, flexibility and problem solving; e.g., "I can solve problems in different ways"); and general mood (happiness and optimism; e.g., "I feel good about myself"). Cronbach's alpha for each of the scales was: 0.90 for intrapersonal; 0.75 for interpersonal; 0.82 for stress management; 0.82 in adaptability; and 0.87 for general mood.

Procedure
Prior to collecting data, we assured the participants that the treatment of data in the study would comply with applicable standards of data security, confidentiality, and ethics. The study was approved by the Bioethics Committee of the University of Almería (Spain) (ethic code: UALBIO2017/011). The application of the questionnaire was done through a web platform which allowed subjects to complete them online. A series of control questions were included to monitor for random or incongruent responses, which were removed from the study.

Data Analysis
We confirmed the univariate normality of the sample following the criteria in which the maximum allowed values for asymmetry and kurtosis are 2 and 7 respectively, and the multivariate normality whit the use of Kolmogorov-Smirnov, obtaining values greater than p < 0.05 in all the variables. We first analyzed sociodemographic variables such as gender, age, and number of children. To identify significant differences between men and women, we used the Student's t-test for independent samples of the components of emotional intelligence and for each dimension of engagement. In order to identify the relationships between those variables and the subjects' ages and numbers of children, we calculated the Pearson correlation coefficient.
In order to understand how the predictor variables (emotional intelligence: intrapersonal, interpersonal, stress management, adaptability, and general mood) related to the criterion variable (engagement: vigor, dedication, and absorption), we performed a stepwise multiple linear regression analysis. Finally, we performed a nonlinear predictive CHAID (chi-square automatic interaction detector) regression and constructed a classification tree. In order to do so, we used the median engagement score (Md = 11.67) from all items. Scores below 11.67 were included in the low engagement group, and scores greater than or equal to 11.67 were included in the high engagement group. All analyses were performed using SPSS ver. 23.0 (IBM Corp, Armonk, NY, USA) statistical software for Windows. Finally, to identify mediation models for estimating the effects on engagement dimensions, a simple moderation analysis is carried out for each of the cases. To do this, the SPSS macro was used to compute models of simple moderation effects [41]. In addition, the bootstrapping technique with estimated coefficients from 5000 bootstrap samples was applied. Table 1 shows the descriptive statistics for the sample as a whole and according to sex. It shows statistically significant differences, in some of the emotional intelligence components: intrapersonal: t (2124) = −4.315, p < 0.001; interpersonal: t (2124) = −4.609; p < 0.001; and adaptability: t (2124) = 2.040; p < 0.05.
Finally, we found negative correlations between the number of children with the emotional intelligence Interpersonal factor (r = −0.06, p < 0.01), and with the engagement dimensions of vigor (r = −0.04, p < 0.05) and (r = −0.05, p < 0.05) absorption.

Components of Emotional Intelligence as Predictors of Engagement in Nurses
The correlation coefficients we calculated showed that nurses with high levels of emotional intelligence also exhibited higher scores in engagement. The correlation analysis showed that all of the emotional intelligence components were positively correlated with each of the engagement dimensions, with correlation indices ranging from r = 0.15 to r = 0.40, and p < 0.001 in all cases. Table 2 shows that the regression analysis for the engagement dimension vigor gave four models, the fourth of which demonstrated the greatest explanatory power, with 22.8% (R 2 = 0.228) of the variance explained by the factors in the model. To confirm the validity of the model, we analyzed the independence of the residuals. The Durbin-Watson D statistic was D = 1.964, confirming the absence of positive or negative autocorrelation. The value of t was associated with a probability of error of less than 0.05 in all of the included variables in the model. The standardized coefficients showed that the variable with the greatest explanatory weight was the interpersonal factor. Lastly, the values of tolerance indicators and VIF suggested the absence of collinearity between the variables included in the model.
The analysis of the dedication component produced four models, the fourth of which demonstrated the greatest explanatory power, with 21.9% (R 2 = 0.219) of the variance explained. The Durbin-Watson statistic confirmed the validity of the model (D = 1.941). The value of t was associated with a probability of error of less than 0.05 in all of the included variables in the model. The standardized coefficients indicated that general mood was the strongest predictor of dedication in the sample. The values of tolerance indicators and VIF suggested the absence of collinearity between the variables included in the model.
For the absorption dimension, the regression analysis produced four models, the fourth of which accounted for 14% of the explained variance (R 2 = 0.140) with D = 1.961, confirming the validity of the model. The value of t was associated with a probability of error of less than 0.05 in all of the included variables in the model. In this case, the interpersonal component of emotional intelligence was the strongest predictor of absorption. The values of tolerance indicators and VIF suggested the absence of collinearity between the variables included in the model.
The decision tree (Figure 1) showed that the interpersonal factor was the best predictor of engagement. Participants with low scores in the interpersonal factor and low adaptability exhibited low levels of engagement (79.2%). High levels of engagement were present in those with high scores in the interpersonal variable (79.8%). Finally, the goodness of fit of the model functioning could be seen in its correct classification of 65.7% of the participants.

Mediation Models for Estimating the Effects on Engagement Dimensions
Based on the results of the regression analysis, the emotional intelligence interpersonal factor was taken as the independent predictor variable and mood as the mediating variable. Thus, three simple mediation models were computed with the interpersonal factor as the independent variable in all cases: in the first model, the dependent variable was 'vigor', in the second 'dedication', and in the third, 'absorption' was taken as the dependent variable. Figure 2 shows the simple mediation model for vigor, including the direct, indirect, and total effects. In the first place, it may be observed that there was a statistically significant effect (B Inter = 0.54, p < 0.001) of the interpersonal factor (X) on mood (M). The second regression analysis includes the interpersonal factor (X) and mood (M) in the equation. In both cases statistically significant effects on the dependent variable were found (vigor): M→Y (B E_ánimo = 0.35, p < 0.001) and X→Y (B Inter = 0.41, p < 0.001). With the third regression analysis, the total effect of the independent variable (X) on the dependent variable (Y) was estimated. In this case, a statistically significant effect of the interpersonal factor on the vigor dimension of engagement was found (B Inter = 0.60, p < 0.001). Finally, the analysis of the indirect effect was carried out using bootstrapping, finding data supporting a significant level (B = 0.19, SE = 0.02, 95% CI (0.15, 0.23)). The decision tree (Figure 1) showed that the interpersonal factor was the best predictor of engagement. Participants with low scores in the interpersonal factor and low adaptability exhibited low levels of engagement (79.2%). High levels of engagement were present in those with high scores in the interpersonal variable (79.8%). Finally, the goodness of fit of the model functioning could be seen in its correct classification of 65.7% of the participants.

Mediation Models for Estimating the Effects on Engagement Dimensions
Based on the results of the regression analysis, the emotional intelligence interpersonal factor was taken as the independent predictor variable and mood as the mediating variable. Thus, three simple mediation models were computed with the interpersonal factor as the independent variable in all cases: in the first model, the dependent variable was 'vigor', in the second 'dedication', and in the third, 'absorption' was taken as the dependent variable. Figure 2 shows the simple mediation model for vigor, including the direct, indirect, and total effects. In the first place, it may be observed that there was a statistically significant effect (BInter = 0.54, p < 0.001) of the interpersonal factor (X) on mood (M). The second regression analysis includes the interpersonal factor (X) and mood (M) in the equation. In both cases statistically significant effects on the dependent variable were found (vigor): M→Y (BE_ánimo = 0.35, p < 0.001) and X→Y (BInter = 0.41, p < 0.001). With the third regression analysis, the total effect of the independent variable (X) on the dependent variable (Y) was estimated. In this case, a statistically significant effect of the interpersonal factor on the vigor dimension of engagement was found (BInter = 0.60, p < 0.001). Finally, the analysis of the indirect effect was carried out using bootstrapping, finding data supporting a significant level (B = 0.19, SE = 0.02, 95% CI (0.15, 0.23)).  Figure 3 shows the simple mediation model for dedication. Based on the second regression analysis, the effects of the independent variable (interpersonal) and the mediator (mood) on the dependent variable (dedication) were estimated. It may be observed that in both cases, the effect on dedication was statistically significant: (BInter = 0.37, p < 0.001) and (BE_ánimo = 0.39, p < 0.001). The total effect of the Interpersonal factor on dedication was significant (BInter = 0.59, p < 0.001). Finally, with the analysis of the indirect effect with bootstrapping, data extracted supported a significant level (B = 0.21, SE = 0.02, 95% CI (0.17, 0.26)). Figure 4 shows the simple mediation model for absorption. With the second regression analysis, the effect of the independent variable was estimated taking absorption (Y) as the resulting variable, the effect of the independent variable (BInter = 0.36, p < 0.001) and the mediator (BE_ánimo = 0.26, p < 0.001) were estimated, resulting statistically significant in both cases. The total effect of the interpersonal factor on absorption was significant (BInter = 0.50, p < 0.001). Finally, with the analysis of the indirect effects with bootstrapping, a significant effect was found (B = 0.14, SE = 0.02, 95% CI (0.10, 0.18)).  Figure 3 shows the simple mediation model for dedication. Based on the second regression analysis, the effects of the independent variable (interpersonal) and the mediator (mood) on the dependent variable (dedication) were estimated. It may be observed that in both cases, the effect on dedication was statistically significant: (B Inter = 0.37, p < 0.001) and (B E_ánimo = 0.39, p < 0.001). The total effect of the Interpersonal factor on dedication was significant (B Inter = 0.59, p < 0.001). Finally, with the analysis of the indirect effect with bootstrapping, data extracted supported a significant level (B = 0.21, SE = 0.02, 95% CI (0.17, 0.26)). Figure 4 shows the simple mediation model for absorption. With the second regression analysis, the effect of the independent variable was estimated taking absorption (Y) as the resulting variable, the effect of the independent variable (B Inter = 0.36, p < 0.001) and the mediator (B E_ánimo = 0.26, p < 0.001) were estimated, resulting statistically significant in both cases. The total effect of the interpersonal factor on absorption was significant (B Inter = 0.50, p < 0.001). Finally, with the analysis of the indirect effects with bootstrapping, a significant effect was found (B = 0.14, SE = 0.02, 95% CI (0.10, 0.18)).

Discussion
This study achieved our initial objective by determining the explanatory value of the components of emotional intelligence in engagement in a sample of nurses.
We found that women exhibited higher levels of emotional intelligence in some emotional intelligence components (interpersonal and intrapersonal). These findings agreed with our second hypothesis, where we expected to find significant, positive correlations between emotional intelligence and engagement in nurses. Other research has produced similar results, especially regarding the relationship between the interpersonal [29,31,32] and the intrapersonal dimensions of emotional intelligence [30,31]. Some studies have found higher scores in men for the dimensions of stress management [29], general mood [30], and adaptability [32]. However, it is important to note that men were overrepresented in the samples in those studies [29,32], and in order to draw more safely definitive conclusions, it would be necessary to have samples that were more evenly balanced between men and women.
Our results showed that age was negatively correlated with the interpersonal factor of emotional intelligence and positively correlated with stress management. Harper and Jones-Schenk [33] found that nurses' empathy diminished with age. Kahraman and Hiçdurmaz [34] found a negative correlation between the number of children and the interpersonal factor of emotional intelligence, which was not in agreement with our results.
We found significant differences between the gender in all dimensions of engagement, with women scoring higher; this is in line with other research [19]. The three engagement dimensions were negatively correlated with age, which were similar to findings from other studies [16,18,20]. We also

Discussion
This study achieved our initial objective by determining the explanatory value of the components of emotional intelligence in engagement in a sample of nurses.
We found that women exhibited higher levels of emotional intelligence in some emotional intelligence components (interpersonal and intrapersonal). These findings agreed with our second hypothesis, where we expected to find significant, positive correlations between emotional intelligence and engagement in nurses. Other research has produced similar results, especially regarding the relationship between the interpersonal [29,31,32] and the intrapersonal dimensions of emotional intelligence [30,31]. Some studies have found higher scores in men for the dimensions of stress management [29], general mood [30], and adaptability [32]. However, it is important to note that men were overrepresented in the samples in those studies [29,32], and in order to draw more safely definitive conclusions, it would be necessary to have samples that were more evenly balanced between men and women.
Our results showed that age was negatively correlated with the interpersonal factor of emotional intelligence and positively correlated with stress management. Harper and Jones-Schenk [33] found that nurses' empathy diminished with age. Kahraman and Hiçdurmaz [34] found a negative correlation between the number of children and the interpersonal factor of emotional intelligence, which was not in agreement with our results.
We found significant differences between the gender in all dimensions of engagement, with women scoring higher; this is in line with other research [19]. The three engagement dimensions were negatively correlated with age, which were similar to findings from other studies [16,18,20]. We also

Discussion
This study achieved our initial objective by determining the explanatory value of the components of emotional intelligence in engagement in a sample of nurses.
We found that women exhibited higher levels of emotional intelligence in some emotional intelligence components (interpersonal and intrapersonal). These findings agreed with our second hypothesis, where we expected to find significant, positive correlations between emotional intelligence and engagement in nurses. Other research has produced similar results, especially regarding the relationship between the interpersonal [29,31,32] and the intrapersonal dimensions of emotional intelligence [30,31]. Some studies have found higher scores in men for the dimensions of stress management [29], general mood [30], and adaptability [32]. However, it is important to note that men were over-represented in the samples in those studies [29,32], and in order to draw more safely definitive conclusions, it would be necessary to have samples that were more evenly balanced between men and women.
Our results showed that age was negatively correlated with the interpersonal factor of emotional intelligence and positively correlated with stress management. Harper and Jones-Schenk [33] found that nurses' empathy diminished with age. Kahraman and Hiçdurmaz [34] found a negative correlation between the number of children and the interpersonal factor of emotional intelligence, which was not in agreement with our results.
We found significant differences between the gender in all dimensions of engagement, with women scoring higher; this is in line with other research [19]. The three engagement dimensions were negatively correlated with age, which were similar to findings from other studies [16,18,20]. We also saw negative correlations between the numbers of children and the engagement dimensions of vigor and absorption.
We also achieved our objective of developing an explanatory model of engagement showing that nurses with higher levels of emotional intelligence also scored more highly in engagement, with the interpersonal factor being the greatest predictor of engagement. Other studies support the relationship between the two variables [11,[35][36][37].
Thus, three simple mediation models were computed with the interpersonal factor as the independent variable in all cases: in the first model, the dependent variable was vigor; in the second dedication; and in the third, absorption was taken as the dependent variable. The analysis of the indirect effect was carried out using bootstrapping, finding data supporting a significant level the general mood in all cases.
Our results have significant practical implications for the creation of intervention programs and activities to improve the performance of nurses in the workplace (e.g., skills training programs for managing emotions in relationships with co-workers, patients). The results should, however, be considered with some care due to the following limitations. First, the data were gathered through online questionnaires completed by the nurses and may be biased as the subjects' responses may be subject to desirability bias. Second, as the sample we used was very specific and limited to one type of profession in the healthcare field, it is possible that the results cannot be generalized to other related healthcare professions. Third, the study design did not allow us to determine whether the engagement and emotional intelligence scores remained constant over time. Finally, in Spain, nursing is a predominantly a female profession, which was reflected in the sample, and may be a limitation on the results.
Finally, we are continuing to work on the analysis of elements that encourage worker engagement, and future research should address other variables related to the subject (personality, self-esteem, etc.) and the work environment (such as number of patients dealt with, shift patterns, etc.) in order to continue describing this construct.

Conclusions
The results showed that there were significant differences in emotional intelligence and engagement when we looked at the sociodemographic variables in the study (sex, age, number of children). These findings supported our first research hypothesis, where we expected to find differences in emotional intelligence and engagement according to sociodemographic variables, although the results found in the reviewed literature varied.
Emotional intelligence explained 22.8% of the variability in the engagement dimension vigor, with the interpersonal factor having the greatest explanatory weight. It explained 21.9% of the variability in the dedication dimension, with general mood being the strongest predictor, and explained 14% of the variability in the absorption dimension, with the interpersonal component being the strongest predictor.